Tootfinder

VIMD: Monocular Visual-Inertial Motion and Depth Estimation
Saimouli Katragadda, Guoquan Huang
https://arxiv.org/abs/2509.19713 https://arxiv.org/pdf/2509.…

VIMD: Monocular Visual-Inertial Motion and Depth Estimation
Accurate and efficient dense metric depth estimation is crucial for 3D visual perception in robotics and XR. In this paper, we develop a monocular visual-inertial motion and depth (VIMD) learning framework to estimate dense metric depth by leveraging accurate and efficient MSCKF-based monocular visual-inertial motion tracking. At the core the proposed VIMD is to exploit multi-view information to iteratively refine per-pixel scale, instead of globally fitting an invariant affine model as in the …

Graph-Aware Diffusion for Signal Generation
Sergio Rozada, Vimal K. B., Andrea Cavallo, Antonio G. Marques, Hadi Jamali-Rad, Elvin Isufi
https://arxiv.org/abs/2510.05036 https:/…

Graph-Aware Diffusion for Signal Generation
We study the problem of generating graph signals from unknown distributions defined over given graphs, relevant to domains such as recommender systems or sensor networks. Our approach builds on generative diffusion models, which are well established in vision and graph generation but remain underexplored for graph signals. Existing methods lack generality, either ignoring the graph structure in the forward process or designing graph-aware mechanisms tailored to specific domains. We adopt a forw…

Northern Europe is getting a massive energy storage boost. EVE Energy and Vimab BESS just signed a deal to deploy 1.48 gigawatt-hours of battery storage across Sweden and neighboring countries.
Why it matters: As wind and solar capacity grows in the Nordics, large-scale storage is crucial for grid stability and reliable power supply.

1.48 GWh partnership to drive energy transition forward
EVE Energy and Vimab BESS are to roll out 1.48 GWh of energy storage in Northern Europe to support the region’s clean energy transition.

A Noise Resilient Approach for Robust Hurst Exponent Estimation
Malith Premarathna (Department of Statistics, University of Nebraska-Lincoln), Fabrizio Ruggeri (CNR IMATI, Milano), Dixon Vimalajeewa (Department of Statistics, University of Nebraska-Lincoln)
https://arxiv.org/abs/2510.04811

A Noise Resilient Approach for Robust Hurst Exponent Estimation
Understanding signal behavior across scales is vital in areas such as natural phenomena analysis and financial modeling. A key property is self-similarity, quantified by the Hurst exponent (H), which reveals long-term dependencies. Wavelet-based methods are effective for estimating H due to their multi-scale analysis capability, but additive noise in real-world measurements often degrades accuracy. We propose Noise-Controlled ALPHEE (NC-ALPHEE), an enhancement of the Average Level-Pairwise Hurs…

Bunching of extreme events on complex network
Sarvesh K. Upadhyay, Vimal Kishore, Sanjay Kumar, R. E. Amritkar
https://arxiv.org/abs/2509.18764 https://arx…